{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NielsRogge/Transformers-Tutorials/blob/master/PaliGemma/Fine_tune_PaliGemma_for_image_%3EJSON.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j1S447KSf6YH"
      },
      "source": [
        "# Fine-tune PaliGemma for image -> JSON use cases\n",
        "\n",
        "In this notebook, we'll fine-tune Google's [PaliGemma](https://huggingface.co/docs/transformers/main/en/model_doc/paligemma), an open vision-language model, on a custom dataset where the goal for the model is to turn a receipt image into a JSON containing all fields.\n",
        "\n",
        "<img src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/paligemma/paligemma_arch.png\"\n",
        "alt=\"drawing\" width=\"600\"/>\n",
        "\n",
        "<small> PaliGemma architecture. Taken from the <a href=\"https://huggingface.co/blog/paligemma\">blog post.</a> </small>\n",
        "\n",
        "## Set-up environment\n",
        "\n",
        "Let's start by installing the necessary libraries. We install 🤗 Transformers from source here since some updates were included which are not yet in a PyPi release."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O8YdyxBPhmjO",
        "outputId": "06345377-5b47-4731-f877-1cbc3c13ddd1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install -q --upgrade git+https://github.com/huggingface/transformers.git"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For loading a dataset, we will use 🤗 Datasets. For training the model, we will use PyTorch Lightning (this could of course be replaced by native PyTorch, 🤗 Accelerate or the 🤗 Trainer API)."
      ],
      "metadata": {
        "id": "-pBviBNng762"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "jv3zDH7Gfv1Q"
      },
      "outputs": [],
      "source": [
        "!pip install -q datasets lightning"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In order to train the model in a [parameter-efficient way](https://huggingface.co/blog/4bit-transformers-bitsandbytes), we will leverage the 🤗 PEFT library (which stands for Parameter-Efficient Fine-tuning) along with Accelerate and Bitsandbytes."
      ],
      "metadata": {
        "id": "KmH5APIchAta"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q peft accelerate bitsandbytes"
      ],
      "metadata": {
        "id": "WKYWxoYhTLnq"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We'll also use Weights and Biases for logging (seeing our loss going down during training)."
      ],
      "metadata": {
        "id": "lEJbkxdORFAh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q --upgrade wandb"
      ],
      "metadata": {
        "id": "qiyfiBAHRDra"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sDq_KVPMxQL1"
      },
      "source": [
        "## Define variables\n",
        "\n",
        "Here we define some variables which we'll use throughout this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "n780Oon8xQEY"
      },
      "outputs": [],
      "source": [
        "REPO_ID = \"google/paligemma-3b-pt-224\"\n",
        "FINETUNED_MODEL_ID = \"nielsr/paligemma-cord-demo\"\n",
        "MAX_LENGTH = 512\n",
        "WANDB_PROJECT = \"paligemma\"\n",
        "WANDB_NAME = \"cord-demo\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4AoVFrvsf-qV"
      },
      "source": [
        "## Load dataset\n",
        "\n",
        "Let's start by loading the dataset from the hub. Here we use the [CORD](https://huggingface.co/datasets/naver-clova-ix/cord-v2) dataset, created by the [Donut](https://huggingface.co/docs/transformers/en/model_doc/donut) authors (Donut is another powerful document AI model available in the Transformers library). CORD is an important benchmark for receipt understanding. The Donut authors have prepared it in a format that suits vision-language models: we're going to fine-tune it to generate the JSON given the image.\n",
        "\n",
        "If you want to load your own custom dataset, check out this guide: https://huggingface.co/docs/datasets/image_dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "1cISUugif160"
      },
      "outputs": [],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "dataset = load_dataset(\"naver-clova-ix/cord-v2\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's check out the dataset:"
      ],
      "metadata": {
        "id": "HqMO_3EYMQLe"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ADyrswxJf5go",
        "outputId": "6bc119ca-b331-4084-d865-79cc25caeb77"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['image', 'ground_truth'],\n",
              "        num_rows: 800\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['image', 'ground_truth'],\n",
              "        num_rows: 100\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['image', 'ground_truth'],\n",
              "        num_rows: 100\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As oftentimes, we get a `DatasetDict` which is a dictionary containing 3 splits, one for training, validation and testing. Each split has 2 features, an image and a corresponding ground truth.\n",
        "\n",
        "Let's check the first training example:"
      ],
      "metadata": {
        "id": "E1wuJlBrMTY-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "example = dataset['train'][0]\n",
        "image = example[\"image\"]\n",
        "# resize image for smaller displaying\n",
        "width, height = image.size\n",
        "image = image.resize((int(0.3*width), int(0.3*height)))\n",
        "image"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        },
        "id": "0rcJBAF3MTGf",
        "outputId": "e0348cd9-3d6f-4e6f-f97f-bcc01146ecfc"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=259x388>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's check the corresponding ground truth, which we can read as JSON:"
      ],
      "metadata": {
        "id": "shCwba5zMYHa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "\n",
        "ground_truth = json.loads(example[\"ground_truth\"])\n",
        "ground_truth[\"gt_parse\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bKrJtxEPMWlw",
        "outputId": "35e22fc7-92fe-4942-e9bf-c32c9ace4e7f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'menu': [{'nm': 'Nasi Campur Bali', 'cnt': '1 x', 'price': '75,000'},\n",
              "  {'nm': 'Bbk Bengil Nasi', 'cnt': '1 x', 'price': '125,000'},\n",
              "  {'nm': 'MilkShake Starwb', 'cnt': '1 x', 'price': '37,000'},\n",
              "  {'nm': 'Ice Lemon Tea', 'cnt': '1 x', 'price': '24,000'},\n",
              "  {'nm': 'Nasi Ayam Dewata', 'cnt': '1 x', 'price': '70,000'},\n",
              "  {'nm': 'Free Ice Tea', 'cnt': '3 x', 'price': '0'},\n",
              "  {'nm': 'Organic Green Sa', 'cnt': '1 x', 'price': '65,000'},\n",
              "  {'nm': 'Ice Tea', 'cnt': '1 x', 'price': '18,000'},\n",
              "  {'nm': 'Ice Orange', 'cnt': '1 x', 'price': '29,000'},\n",
              "  {'nm': 'Ayam Suir Bali', 'cnt': '1 x', 'price': '85,000'},\n",
              "  {'nm': 'Tahu Goreng', 'cnt': '2 x', 'price': '36,000'},\n",
              "  {'nm': 'Tempe Goreng', 'cnt': '2 x', 'price': '36,000'},\n",
              "  {'nm': 'Tahu Telor Asin', 'cnt': '1 x', 'price': '40,000.'},\n",
              "  {'nm': 'Nasi Goreng Samb', 'cnt': '1 x', 'price': '70,000'},\n",
              "  {'nm': 'Bbk Panggang Sam', 'cnt': '3 x', 'price': '366,000'},\n",
              "  {'nm': 'Ayam Sambal Hija', 'cnt': '1 x', 'price': '92,000'},\n",
              "  {'nm': 'Hot Tea', 'cnt': '2 x', 'price': '44,000'},\n",
              "  {'nm': 'Ice Kopi', 'cnt': '1 x', 'price': '32,000'},\n",
              "  {'nm': 'Tahu Telor Asin', 'cnt': '1 x', 'price': '40,000'},\n",
              "  {'nm': 'Free Ice Tea', 'cnt': '1 x', 'price': '0'},\n",
              "  {'nm': 'Bebek Street', 'cnt': '1 x', 'price': '44,000'},\n",
              "  {'nm': 'Ice Tea Tawar', 'cnt': '1 x', 'price': '18,000'}],\n",
              " 'sub_total': {'subtotal_price': '1,346,000',\n",
              "  'service_price': '100,950',\n",
              "  'tax_price': '144,695',\n",
              "  'etc': '-45'},\n",
              " 'total': {'total_price': '1,591,600'}}"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is what we want the model to learn given an image."
      ],
      "metadata": {
        "id": "a3LgxQ_GMmts"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PReeTqFEf_mN"
      },
      "source": [
        "## Create PyTorch datasets\n",
        "\n",
        "Next we'll create regular [PyTorch datasets](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html) which define the individual items of the dataset. For that, one needs to implement 3 methods: an `init` method, a `len` method (which returns the length of the dataset) and a `getitem` method (which returns items of the dataset).\n",
        "\n",
        "Relevant here is the `json2token` method which turns each JSON target sequence into a token sequence which the model can learn to generate."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "XVhvWOOFf6Ja"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import Dataset\n",
        "from typing import Any, List, Dict\n",
        "import random\n",
        "import json\n",
        "\n",
        "\n",
        "class CustomDataset(Dataset):\n",
        "    \"\"\"\n",
        "    PyTorch Dataset. This class takes a HuggingFace Dataset as input.\n",
        "\n",
        "    Each row, consists of image path(png/jpg/jpeg) and gt data (json/jsonl/txt).\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        dataset_name_or_path: str,\n",
        "        split: str = \"train\",\n",
        "        sort_json_key: bool = True,\n",
        "    ):\n",
        "        super().__init__()\n",
        "\n",
        "        self.split = split\n",
        "        self.sort_json_key = sort_json_key\n",
        "\n",
        "        self.dataset = load_dataset(dataset_name_or_path, split=self.split)\n",
        "        self.dataset_length = len(self.dataset)\n",
        "\n",
        "        self.gt_token_sequences = []\n",
        "        for sample in self.dataset:\n",
        "            ground_truth = json.loads(sample[\"ground_truth\"])\n",
        "            if \"gt_parses\" in ground_truth:  # when multiple ground truths are available, e.g., docvqa\n",
        "                assert isinstance(ground_truth[\"gt_parses\"], list)\n",
        "                gt_jsons = ground_truth[\"gt_parses\"]\n",
        "            else:\n",
        "                assert \"gt_parse\" in ground_truth and isinstance(ground_truth[\"gt_parse\"], dict)\n",
        "                gt_jsons = [ground_truth[\"gt_parse\"]]\n",
        "\n",
        "            self.gt_token_sequences.append(\n",
        "                [\n",
        "                    self.json2token(\n",
        "                        gt_json,\n",
        "                        sort_json_key=self.sort_json_key,\n",
        "                    )\n",
        "                    for gt_json in gt_jsons  # load json from list of json\n",
        "                ]\n",
        "            )\n",
        "\n",
        "    def json2token(self, obj: Any, sort_json_key: bool = True):\n",
        "        \"\"\"\n",
        "        Convert an ordered JSON object into a token sequence\n",
        "        \"\"\"\n",
        "        if type(obj) == dict:\n",
        "            if len(obj) == 1 and \"text_sequence\" in obj:\n",
        "                return obj[\"text_sequence\"]\n",
        "            else:\n",
        "                output = \"\"\n",
        "                if sort_json_key:\n",
        "                    keys = sorted(obj.keys(), reverse=True)\n",
        "                else:\n",
        "                    keys = obj.keys()\n",
        "                for k in keys:\n",
        "                    output += (\n",
        "                        fr\"<s_{k}>\"\n",
        "                        + self.json2token(obj[k], sort_json_key)\n",
        "                        + fr\"</s_{k}>\"\n",
        "                    )\n",
        "                return output\n",
        "        elif type(obj) == list:\n",
        "            return r\"<sep/>\".join(\n",
        "                [self.json2token(item, sort_json_key) for item in obj]\n",
        "            )\n",
        "        else:\n",
        "            obj = str(obj)\n",
        "            return obj\n",
        "\n",
        "    def __len__(self) -> int:\n",
        "        return self.dataset_length\n",
        "\n",
        "    def __getitem__(self, idx: int) -> Dict:\n",
        "        \"\"\"\n",
        "        Returns one item of the dataset.\n",
        "\n",
        "        Returns:\n",
        "            image : the original Receipt image\n",
        "            target_sequence : tokenized ground truth sequence\n",
        "        \"\"\"\n",
        "        sample = self.dataset[idx]\n",
        "\n",
        "        # inputs\n",
        "        image = sample[\"image\"]\n",
        "        target_sequence = random.choice(self.gt_token_sequences[idx])  # can be more than one, e.g., DocVQA Task 1\n",
        "\n",
        "        return image, target_sequence"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next we instantiate both the training and validation datasets:"
      ],
      "metadata": {
        "id": "Xq9qFeNSM1rf"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "yrAkQcLogZrV"
      },
      "outputs": [],
      "source": [
        "train_dataset = CustomDataset(\"naver-clova-ix/cord-v2\", split=\"train\")\n",
        "val_dataset = CustomDataset(\"naver-clova-ix/cord-v2\", split=\"validation\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SJtsGCjfgAzL"
      },
      "source": [
        "## Create collate functions\n",
        "\n",
        "Now that we have a PyTorch dataset, we'll define so-called collators which define how items of the dataset should be batched together. This is because we typically train neural networks on batches of data (i.e. various images/target sequences combined) rather than one-by-one, using a variant of stochastic-gradient descent or SGD (like [Adam](https://pytorch.org/docs/stable/generated/torch.optim.Adam.html), [AdamW](https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html), etc.).\n",
        "\n",
        "It's only here that we're going to use the [processor](https://huggingface.co/docs/transformers/main/en/model_doc/paligemma#transformers.PaliGemmaProcessor) which can be used to prepare the image and text inputs along with the text targets for the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "xvJ3F43ohM2E"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoProcessor\n",
        "\n",
        "processor = AutoProcessor.from_pretrained(REPO_ID)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We define a separate collate function for training vs. evaluation. During training, we need to feed the labels in order to calculate the loss, whereas during evaluation we only feed the prompt along with the image to the model and let it autoregressively generate a completion, which we can compare against the ground truth answer.\n",
        "\n",
        "We use a custom prompt here, feel free to change this."
      ],
      "metadata": {
        "id": "T9LFGNPhNS91"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "msipzECZgArV"
      },
      "outputs": [],
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "PROMPT = \"extract JSON.\"\n",
        "\n",
        "def train_collate_fn(examples):\n",
        "  images = [example[0] for example in examples]\n",
        "  texts = [PROMPT for _ in range(len(images))]\n",
        "  labels = [example[1] for example in examples]\n",
        "\n",
        "  inputs = processor(text=texts, images=images, suffix=labels, return_tensors=\"pt\", padding=True,\n",
        "                     truncation=\"only_second\", max_length=MAX_LENGTH,\n",
        "                     tokenize_newline_separately=False)\n",
        "\n",
        "  input_ids = inputs[\"input_ids\"]\n",
        "  token_type_ids = inputs[\"token_type_ids\"]\n",
        "  attention_mask = inputs[\"attention_mask\"]\n",
        "  pixel_values = inputs[\"pixel_values\"]\n",
        "  labels = inputs[\"labels\"]\n",
        "\n",
        "  return input_ids, token_type_ids, attention_mask, pixel_values, labels\n",
        "\n",
        "\n",
        "def eval_collate_fn(examples):\n",
        "  images = [example[0] for example in examples]\n",
        "  texts = [PROMPT for _ in range(len(images))]\n",
        "  answers = [example[1] for example in examples]\n",
        "\n",
        "  inputs = processor(text=texts, images=images, return_tensors=\"pt\", padding=True, tokenize_newline_separately=False)\n",
        "\n",
        "  input_ids = inputs[\"input_ids\"]\n",
        "  attention_mask = inputs[\"attention_mask\"]\n",
        "  pixel_values = inputs[\"pixel_values\"]\n",
        "\n",
        "  return input_ids, attention_mask, pixel_values, answers"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As always, it's super important to verify your data before feeding it to a model. Here I'm verifying the collate function by creating a [PyTorch dataloader](https://pytorch.org/docs/stable/data.html), which gives us batches of data. We take the first batch of data and check whether everything is prepared in the right format for the model."
      ],
      "metadata": {
        "id": "HnF5i4txNhMp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train_dataloader = DataLoader(train_dataset, collate_fn=train_collate_fn, batch_size=2, shuffle=True)\n",
        "input_ids, token_type_ids, attention_mask, pixel_values, labels = next(iter(train_dataloader))"
      ],
      "metadata": {
        "id": "OkfwzgWAU2yW"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's see which tokens the model gets as input (the `input_ids`). We can see that padding is done on the left side (to make sure the inputs can be batched to the same length). The model gets a sequence of padding tokens, image tokens and then the actual text as input.\n",
        "\n",
        "Internally, the model will replace the special image tokens by embeddings from the vision encoder."
      ],
      "metadata": {
        "id": "mBe4UAFNN0ch"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qOMbDP4lvuyP",
        "outputId": "12abebff-1dfe-4183-b85b-c08b85cc4401"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['<pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><bos>extract JSON.\\n<s_total><s_total_price>9.500,00</s_total_price><s_menuqty_cnt>1</s_menuqty_cnt><s_changeprice>10.500,00</s_changeprice><s_cashprice>20.000,00</s_cashprice></s_total><s_menu><s_unitprice>9.500,00</s_unitprice><s_price>9.500,00</s_price><s_num>2005</s_num><s_nm>CHEESE JOHN</s_nm><s_cnt>x1</s_cnt></s_menu><eos>',\n",
              " '<image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><bos>extract JSON.\\n<s_total><s_total_price>39,200</s_total_price><s_changeprice>60,800</s_changeprice><s_cashprice>100,000</s_cashprice></s_total><s_sub_total><s_subtotal_price>39,200</s_subtotal_price></s_sub_total><s_menu><s_unitprice>@ 15000</s_unitprice><s_price>30,000</s_price><s_nm>LAPIS SINGAPUR</s_nm><s_discountprice>-9,000</s_discountprice><s_cnt>2X</s_cnt><sep/><s_unitprice>@9000</s_unitprice><s_price>18,000</s_price><s_nm>LEMPER</s_nm><s_discountprice>-5,400</s_discountprice><s_cnt>2X</s_cnt><sep/><s_price>8,000</s_price><s_nm>AREM AREM</s_nm><s_discountprice>-2,400</s_discountprice><sep/><s_price>0</s_price><s_nm>MIKA BESAR</s_nm><sep/><s_price>0</s_price><s_nm>PLASTIK SEDANG</s_nm></s_menu><eos>']"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "processor.batch_decode(input_ids)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's check the corresponding labels:"
      ],
      "metadata": {
        "id": "ulAnuUiMN6xV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for id, label in zip(input_ids[0][-30:], labels[0][-30:]):\n",
        "  print(processor.decode([id.item()]), processor.decode([label.item()]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xpS8w0dlN8mh",
        "outputId": "97ab459a-8482-4648-a3e5-7d20c0fbae57"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "num num\n",
            ">< ><\n",
            "s s\n",
            "_ _\n",
            "nm nm\n",
            "> >\n",
            "CHE CHE\n",
            "ESE ESE\n",
            " JOHN  JOHN\n",
            "</ </\n",
            "s s\n",
            "_ _\n",
            "nm nm\n",
            ">< ><\n",
            "s s\n",
            "_ _\n",
            "cnt cnt\n",
            "> >\n",
            "x x\n",
            "1 1\n",
            "</ </\n",
            "s s\n",
            "_ _\n",
            "cnt cnt\n",
            "></ ></\n",
            "s s\n",
            "_ _\n",
            "menu menu\n",
            "> >\n",
            "<eos> <eos>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can do the same for the validation collate function:"
      ],
      "metadata": {
        "id": "RGYdQ8TSOXL-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "val_dataloader = DataLoader(val_dataset, collate_fn=eval_collate_fn, batch_size=2, shuffle=False)\n",
        "input_ids, attention_mask, pixel_values, answers = next(iter(val_dataloader))"
      ],
      "metadata": {
        "id": "SeEv0b0HU4vM"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "processor.batch_decode(input_ids)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1YF1ktrIXyvm",
        "outputId": "176ee519-989b-4cb0-828f-e0bb1dda69d5"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['<image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><bos>extract JSON.\\n',\n",
              " '<image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><image><bos>extract JSON.\\n']"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3AJGsRerv7OF"
      },
      "source": [
        "## Define PyTorch LightningModule\n",
        "\n",
        "There are various ways to train a PyTorch model: one could just use native PyTorch, use the [Trainer API](https://huggingface.co/docs/transformers/en/main_classes/trainer) or frameworks like [Accelerate](https://huggingface.co/docs/accelerate/en/index). In this notebook, I'll use PyTorch Lightning as it allows to easily compute evaluation metrics during training.\n",
        "\n",
        "Below, we define a [LightningModule](https://lightning.ai/docs/pytorch/stable/common/lightning_module.html), which is the standard way to train a model in PyTorch Lightning. A LightningModule is an `nn.Module` with some additional functionality.\n",
        "\n",
        "Basically, PyTorch Lightning will take care of all device placements (`.to(device)`) for us, as well as the backward pass, putting the model in training mode, etc.\n",
        "\n",
        "Notice the difference between a training step and an evaluation step:\n",
        "\n",
        "- a training step only consists of a forward pass, in which we compute the cross-entropy loss between the model's next token predictions and the ground truth (in parallel for all tokens, this technique is known as \"teacher forcing\"). The backward pass is handled by PyTorch Lightning.\n",
        "- an evaluation step consists of making the model autoregressively complete the prompt using the [`generate()`](https://huggingface.co/docs/transformers/v4.40.1/en/main_classes/text_generation#transformers.GenerationMixin.generate) method. After that, we compute an evaluation metric between the predicted sequences and the ground truth ones. This allows us to see how the model is improving over the course of training. The metric we use here is the so-called [Levenhstein edit distance](https://en.wikipedia.org/wiki/Levenshtein_distance). This quantifies how much we would need to edit the predicted token sequence to get the target sequence (the fewer edits the better!). Its optimal value is 0 (which means, no edits need to be made).\n",
        "\n",
        "Besides that, we define the optimizer to use ([AdamW](https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html) is a good default choice) and the data loaders, which use the collate functions defined above to batch together items of the PyTorch datasets. Do note that AdamW is a pretty heavy optimizer in terms of memory requirements, but as we're training with QLoRa as you'll see below we only need to store optimizer states for the adapter layers. For full fine-tuning, one could take a look at more memory friendly optimizers such as [8-bit Adam](https://huggingface.co/docs/bitsandbytes/main/en/optimizers)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "xlI677sZvyIa"
      },
      "outputs": [],
      "source": [
        "import lightning as L\n",
        "import torch\n",
        "from torch.utils.data import DataLoader\n",
        "import re\n",
        "from nltk import edit_distance\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "class PaliGemmaModelPLModule(L.LightningModule):\n",
        "    def __init__(self, config, processor, model):\n",
        "        super().__init__()\n",
        "        self.config = config\n",
        "        self.processor = processor\n",
        "        self.model = model\n",
        "\n",
        "        self.batch_size = config.get(\"batch_size\")\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "\n",
        "        input_ids, token_type_ids, attention_mask, pixel_values, labels = batch\n",
        "\n",
        "        outputs = self.model(input_ids=input_ids,\n",
        "                                attention_mask=attention_mask,\n",
        "                                token_type_ids=token_type_ids,\n",
        "                                pixel_values=pixel_values,\n",
        "                                labels=labels)\n",
        "        loss = outputs.loss\n",
        "\n",
        "        self.log(\"train_loss\", loss)\n",
        "\n",
        "        return loss\n",
        "\n",
        "    def validation_step(self, batch, batch_idx, dataset_idx=0):\n",
        "\n",
        "        input_ids, attention_mask, pixel_values, answers = batch\n",
        "\n",
        "        # autoregressively generate token IDs\n",
        "        generated_ids = self.model.generate(input_ids=input_ids, attention_mask=attention_mask,\n",
        "                                       pixel_values=pixel_values, max_new_tokens=MAX_LENGTH)\n",
        "        # turn them back into text, chopping of the prompt\n",
        "        # important: we don't skip special tokens here, because we want to see them in the output\n",
        "        predictions = self.processor.batch_decode(generated_ids[:, input_ids.size(1):], skip_special_tokens=True)\n",
        "\n",
        "        scores = []\n",
        "        for pred, answer in zip(predictions, answers):\n",
        "            pred = re.sub(r\"(?:(?<=>) | (?=</s_))\", \"\", pred)\n",
        "            scores.append(edit_distance(pred, answer) / max(len(pred), len(answer)))\n",
        "\n",
        "            if self.config.get(\"verbose\", False) and len(scores) == 1:\n",
        "                print(f\"Prediction: {pred}\")\n",
        "                print(f\"    Answer: {answer}\")\n",
        "                print(f\" Normed ED: {scores[0]}\")\n",
        "\n",
        "        self.log(\"val_edit_distance\", np.mean(scores))\n",
        "\n",
        "        return scores\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        # you could also add a learning rate scheduler if you want\n",
        "        optimizer = torch.optim.AdamW(self.parameters(), lr=self.config.get(\"lr\"))\n",
        "\n",
        "        return optimizer\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        return DataLoader(train_dataset, collate_fn=train_collate_fn, batch_size=self.batch_size, shuffle=True, num_workers=4)\n",
        "\n",
        "    def val_dataloader(self):\n",
        "        return DataLoader(val_dataset, collate_fn=eval_collate_fn, batch_size=self.batch_size, shuffle=False, num_workers=4)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wJj0vZaUxICu"
      },
      "source": [
        "## Load model\n",
        "\n",
        "Next, we're going to load the PaliGemma model from the [hub](https://huggingface.co/google/paligemma-3b-pt-224). This is a model with 3 billion trainable parameters. Do note that we load a model here which already has only been pre-trained (PT).\n",
        "\n",
        "### Full fine-tuning, LoRa and Q-LoRa\n",
        "\n",
        "As this model has 3 billion trainable parameters, that's going to have quite an impact on the amount of memory used. For reference, fine-tuning a model using the [AdamW optimizer](https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html#torch.optim.AdamW) (which is often used to optimize neural networks) with mixed precision, you need about 18 times the amount of parameters in GB of GPU RAM. So in this case, we would need 18x3 billion bytes = 54 GB of GPU RAM if we want to update all the parameters of the model, which would require either 2 L4 GPUs for instance or a single A100/H100 which can get expensive.\n",
        "\n",
        "Luckily, some clever people came up with the [LoRa](https://huggingface.co/docs/peft/main/en/conceptual_guides/lora) method (LoRa is short for low-rank adapation). It allows to just freeze the existing weights and only train a couple of adapter layers on top of the base model. Hugging Face offers the separate [PEFT library](https://huggingface.co/docs/peft/main/en/index) for easy use of LoRa, along with other Parameter-Efficient Fine-Tuning methods (that's where the name PEFT comes from).\n",
        "\n",
        "Moreover, one can not only freeze the existing base model but also quantize it (which means, shrinking down its size). A neural network's parameters are typically saved in either float32 (which means, 32 bits or 4 bytes are used to store each parameter value) or float16 (which means, 16 bits or half a byte - also called half precision). However, with some clever algorithms one can shrink each parameter to just 8 or 4 bits (half a byte!), without significant effect on final performance. Read all about it here: https://huggingface.co/blog/4bit-transformers-bitsandbytes.\n",
        "\n",
        "This means that we're going to shrink the size of the base 3B model considerably using 4-bit quantization, and then only train a couple of adapter layers on top using LoRa (in float16). This idea of combining LoRa with quantization is called Q-LoRa and is the most memory friendly version. There exist many forms of quantization, here we leverage the [BitsAndBytes](https://huggingface.co/docs/transformers/main_classes/quantization#transformers.BitsAndBytesConfig) integration.\n",
        "\n",
        "Of course, if you have the memory available, feel free to use full fine-tuning or LoRa without quantization! In case of full fine-tuning, we only train the language model and freeze the vision encoder (SigLIP) and multimodal projector."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "YLP5F1GzxJep"
      },
      "outputs": [],
      "source": [
        "from transformers import PaliGemmaForConditionalGeneration\n",
        "\n",
        "# use this for full fine-tuning\n",
        "# model = PaliGemmaForConditionalGeneration.from_pretrained(REPO_ID)\n",
        "\n",
        "# for param in model.vision_tower.parameters():\n",
        "#     param.requires_grad = False\n",
        "\n",
        "# for param in model.multi_modal_projector.parameters():\n",
        "#     param.requires_grad = False"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import BitsAndBytesConfig\n",
        "from peft import get_peft_model, LoraConfig\n",
        "\n",
        "# use this for Q-LoRa\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "        load_in_4bit=True,\n",
        "        bnb_4bit_quant_type=\"nf4\",\n",
        "        bnb_4bit_compute_type=torch.bfloat16\n",
        ")\n",
        "\n",
        "lora_config = LoraConfig(\n",
        "    r=8,\n",
        "    target_modules=[\"q_proj\", \"o_proj\", \"k_proj\", \"v_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"],\n",
        "    task_type=\"CAUSAL_LM\",\n",
        ")\n",
        "model = PaliGemmaForConditionalGeneration.from_pretrained(REPO_ID, quantization_config=bnb_config, device_map={\"\":0})\n",
        "model = get_peft_model(model, lora_config)\n",
        "model.print_trainable_parameters()\n",
        "#trainable params: 11,298,816 || all params: 2,934,634,224 || trainable%: 0.38501616002417344"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "f1c82f7aaf7747a6b903ff5b188a7495",
            "63964e89c12646aaba29cf71b781e63e",
            "3107e34046a7401a9222014af9ab81f8",
            "7b18de92606045b08a98fc56cd4e928b",
            "5c0c82cf946d406eb271316258b4cd09",
            "3b6d1217d8324cfb89e66c437b9d419d",
            "8c919bc3087848ebb955919634a49c5b",
            "c1c56b85061a4f7eaf5ec96b8ac040c0",
            "3f27b12cd0f74661812e1df4c0e4316c",
            "b4ae1850e253459b9048f5056965a71b",
            "f66caddf086e44248eb25310423f679b"
          ]
        },
        "id": "vZhcsTlfTKYR",
        "outputId": "0959cf61-d8c1-48fc-e774-4c2cb1aef253"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Unused kwargs: ['bnb_4bit_compute_type']. These kwargs are not used in <class 'transformers.utils.quantization_config.BitsAndBytesConfig'>.\n",
            "`config.hidden_act` is ignored, you should use `config.hidden_activation` instead.\n",
            "Gemma's activation function will be set to `gelu_pytorch_tanh`. Please, use\n",
            "`config.hidden_activation` if you want to override this behaviour.\n",
            "See https://github.com/huggingface/transformers/pull/29402 for more details.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f1c82f7aaf7747a6b903ff5b188a7495"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 11,298,816 || all params: 2,934,765,296 || trainable%: 0.3850\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Instantiate LightningModule\n",
        "\n",
        "Now that we have defined the LightningModule and loaded the pre-trained model, we can instantiate it. We store all hyperparameters regarding training (such as the number of epochs, batch size, gradient accumulation, etc.) in a dictionary."
      ],
      "metadata": {
        "id": "a75KDBpmOqm7"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "NSrc8L5bwRtf"
      },
      "outputs": [],
      "source": [
        "config = {\"max_epochs\": 10,\n",
        "          # \"val_check_interval\": 0.2, # how many times we want to validate during an epoch\n",
        "          \"check_val_every_n_epoch\": 1,\n",
        "          \"gradient_clip_val\": 1.0,\n",
        "          \"accumulate_grad_batches\": 8,\n",
        "          \"lr\": 1e-4,\n",
        "          \"batch_size\": 2,\n",
        "          # \"seed\":2022,\n",
        "          \"num_nodes\": 1,\n",
        "          \"warmup_steps\": 50,\n",
        "          \"result_path\": \"./result\",\n",
        "          \"verbose\": True,\n",
        "}\n",
        "\n",
        "model_module = PaliGemmaModelPLModule(config, processor, model)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Define callbacks\n",
        "\n",
        "Optionally, Lightning allows to define so-called [callbacks](https://lightning.ai/docs/pytorch/stable/extensions/callbacks.html), which are arbitrary pieces of code that can be executed during training.\n",
        "\n",
        "Here I'm adding a `PushToHubCallback` which will push the model to the [hub](https://huggingface.co/) at the end of every epoch as well as at the end of training. Do note that you could of course also pass the `private=True` flag when pushing to the hub, if you wish to keep your model private. Hugging Face also offers the [Enterprise Hub](https://huggingface.co/enterprise) so that you can easily share models with your colleagues privately in a secure way. Make sure to set your [token](https://huggingface.co/settings/tokens) as an environment variable (which in Colab can be set by default using a [Colab secret](https://medium.com/@parthdasawant/how-to-use-secrets-in-google-colab-450c38e3ec75) - it's very handy!).\n",
        "\n",
        "We'll also use the EarlyStopping callback of Lightning, which will automatically stop training once the evaluation metric (edit distance in our case) doesn't improve after 3 epochs."
      ],
      "metadata": {
        "id": "5SqYkBa0bjoP"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "4QucAwa_xFSc"
      },
      "outputs": [],
      "source": [
        "from lightning.pytorch.callbacks import Callback\n",
        "from lightning.pytorch.callbacks.early_stopping import EarlyStopping\n",
        "\n",
        "from huggingface_hub import HfApi\n",
        "\n",
        "api = HfApi()\n",
        "\n",
        "class PushToHubCallback(Callback):\n",
        "    def on_train_epoch_end(self, trainer, pl_module):\n",
        "        print(f\"Pushing model to the hub, epoch {trainer.current_epoch}\")\n",
        "        pl_module.model.push_to_hub(FINETUNED_MODEL_ID,\n",
        "                                    commit_message=f\"Training in progress, epoch {trainer.current_epoch}\")\n",
        "\n",
        "    def on_train_end(self, trainer, pl_module):\n",
        "        print(f\"Pushing model to the hub after training\")\n",
        "        pl_module.processor.push_to_hub(FINETUNED_MODEL_ID,\n",
        "                                    commit_message=f\"Training done\")\n",
        "        pl_module.model.push_to_hub(FINETUNED_MODEL_ID,\n",
        "                                    commit_message=f\"Training done\")\n",
        "\n",
        "early_stop_callback = EarlyStopping(monitor=\"val_edit_distance\", patience=3, verbose=False, mode=\"min\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train!\n",
        "\n",
        "Alright, we're set to start training! We will also pass the Weights and Biases logger so that we get see some pretty plots of our loss and evaluation metric during training (do note that you may need to log in the first time you run this, see the [docs](https://docs.wandb.ai/guides/integrations/lightning)).\n",
        "\n",
        "Do note that this Trainer class supports many more flags! See the docs: https://lightning.ai/docs/pytorch/stable/api/lightning.pytorch.trainer.trainer.Trainer.html#lightning.pytorch.trainer.trainer.Trainer."
      ],
      "metadata": {
        "id": "hBebiZ3xbk3-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from lightning.pytorch.loggers import WandbLogger\n",
        "\n",
        "# wandb_logger = WandbLogger(project=WANDB_PROJECT, name=WANDB_NAME)\n",
        "\n",
        "trainer = L.Trainer(\n",
        "        accelerator=\"gpu\",\n",
        "        devices=[0],\n",
        "        max_epochs=config.get(\"max_epochs\"),\n",
        "        accumulate_grad_batches=config.get(\"accumulate_grad_batches\"),\n",
        "        check_val_every_n_epoch=config.get(\"check_val_every_n_epoch\"),\n",
        "        gradient_clip_val=config.get(\"gradient_clip_val\"),\n",
        "        precision=\"16-mixed\",\n",
        "        limit_val_batches=5,\n",
        "        num_sanity_val_steps=0,\n",
        "        # logger=wandb_logger,\n",
        "        callbacks=[PushToHubCallback(), early_stop_callback],\n",
        ")\n",
        "\n",
        "trainer.fit(model_module)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        "outputId": "151f6d37-339a-4815-bd8a-af9fd8a04016"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO: Using 16bit Automatic Mixed Precision (AMP)\n",
            "INFO:lightning.pytorch.utilities.rank_zero:Using 16bit Automatic Mixed Precision (AMP)\n",
            "INFO: GPU available: True (cuda), used: True\n",
            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO: TPU available: False, using: 0 TPU cores\n",
            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO: IPU available: False, using: 0 IPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO: HPU available: False, using: 0 HPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n",
            "INFO: You are using a CUDA device ('NVIDIA L4') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
            "INFO:lightning.pytorch.utilities.rank_zero:You are using a CUDA device ('NVIDIA L4') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n",
            "INFO: LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:lightning.pytorch.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO: \n",
            "  | Name  | Type                 | Params\n",
            "-----------------------------------------------\n",
            "0 | model | PeftModelForCausalLM | 1.7 B \n",
            "-----------------------------------------------\n",
            "11.3 M    Trainable params\n",
            "1.7 B     Non-trainable params\n",
            "1.7 B     Total params\n",
            "6,948.584 Total estimated model params size (MB)\n",
            "INFO:lightning.pytorch.callbacks.model_summary:\n",
            "  | Name  | Type                 | Params\n",
            "-----------------------------------------------\n",
            "0 | model | PeftModelForCausalLM | 1.7 B \n",
            "-----------------------------------------------\n",
            "11.3 M    Trainable params\n",
            "1.7 B     Non-trainable params\n",
            "1.7 B     Total params\n",
            "6,948.584 Total estimated model params size (MB)\n",
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training: |          | 0/? [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b74f793682a64e148c0905165a4a6d6e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/nn/modules.py:426: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_dtype=torch.float32 (default). This will lead to slow inference or training speed.\n",
            "  warnings.warn(\n",
            "/usr/lib/python3.10/multiprocessing/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n",
            "  self.pid = os.fork()\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: |          | 0/? [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fee4b9c2a67b49a9ba630367f6490c2a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: <s_total><s_total_price>45,500</s_total_price><s_changeprice>4,500</s_changeprice><s_cashprice>50,000</s_cashprice></s_total><s_sub_total><s_tax_price>4,500</s_tax_price><s_subtotal_price>45,500</s_subtotal_price></s_sub_total><s_menu><s_price>19,500</s_price><s_nm>REAL BANSO</s_nm><s_cnt>1</s_cnt><sep/><s_price>19,000</s_price><s_nm>EGG_TART</s_nm><s_cnt>1</s_cnt></s_menu>\n",
            "    Answer: <s_total><s_total_price>45,500</s_total_price><s_changeprice>4,500</s_changeprice><s_cashprice>50,000</s_cashprice></s_total><s_menu><s_price>16,500</s_price><s_nm>REAL GANACHE</s_nm><s_cnt>1</s_cnt><sep/><s_price>13,000</s_price><s_nm>EGG TART</s_nm><s_cnt>1</s_cnt><sep/><s_price>16,000</s_price><s_nm>PIZZA TOAST</s_nm><s_cnt>1</s_cnt></s_menu>\n",
            " Normed ED: 0.22340425531914893\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/utilities/data.py:77: Trying to infer the `batch_size` from an ambiguous collection. The batch size we found is 2. To avoid any miscalculations, use `self.log(..., batch_size=batch_size)`.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Prediction: <s_total><s_total_price>100,000</s_total_price><s_changeprice>80,000</s_changeprice><s_cashprice>20,000</s_cashprice></s_total><s_sub_total><s_subtotal_price>20,000</s_subtotal_price></s_sub_total><s_menu><s_price>20,000</s_price><s_nm>S-Ovaltine</s_nm><s_cnt>50</s_cnt></s_menu>\n",
            "    Answer: <s_total><s_total_price>20,000</s_total_price><s_changeprice>80,000</s_changeprice><s_cashprice>100,000</s_cashprice></s_total><s_sub_total><s_tax_price>1,818</s_tax_price><s_subtotal_price>18,181</s_subtotal_price></s_sub_total><s_menu><s_vatyn>10% Tax Included</s_vatyn><s_unitprice>20,000</s_unitprice><s_price>20,000</s_price><s_nm>S-Ovaltine 50%</s_nm><s_cnt>1</s_cnt></s_menu>\n",
            " Normed ED: 0.3010471204188482\n",
            "Prediction: <s_total><s_total_price>41.000</s_total_price><s_changeprice>41.000</s_changeprice><s_cashprice>50.000</s_cashprice></s_total><s_sub_total><s_subtotal_price>41.000</s_subtotal_price></s_sub_total><s_menu><s_price>41.000</s_price><s_nm>BBQ Chicken - Sedang</s_nm><s_cnt>1</s_cnt></s_menu>\n",
            "    Answer: <s_total><s_total_price>41,000</s_total_price><s_menuqty_cnt>1</s_menuqty_cnt><s_changeprice>:9,000</s_changeprice><s_cashprice>50.000</s_cashprice></s_total><s_sub_total><s_subtotal_price>41,000</s_subtotal_price></s_sub_total><s_menu><s_sub><s_price>0</s_price><s_nm>Sedang</s_nm><s_cnt>1</s_cnt></s_sub><s_price>41,000</s_price><s_nm>BBQ Chicken</s_nm><s_cnt>1</s_cnt></s_menu>\n",
            " Normed ED: 0.3078947368421053\n",
            "Prediction: <s_total><s_total_price>123,000</s_total_price></s_total><s_menu><s_price>19,000</s_price><s_nm>POTATO SAUSAGE BREAD</s_nm><s_cnt>1</s_cnt><sep/><s_price>12,000</s_price><s_nm>GREEN TEA SPRIGG</s_nm><s_cnt>1</s_cnt></s_menu>\n",
            "    Answer: <s_total><s_total_price>123,000</s_total_price><s_creditcardprice>123,000</s_creditcardprice></s_total><s_menu><s_price>19,000</s_price><s_nm>POTATO SAUSAGE BREAD</s_nm><s_cnt>1</s_cnt><sep/><s_price>52,000</s_price><s_nm>OREO GREEN TEA SPREAD</s_nm><s_cnt>1</s_cnt><sep/><s_price>52,000</s_price><s_nm>WHITE CHOCO BANANA SPREAD</s_nm><s_cnt>1</s_cnt></s_menu>\n",
            " Normed ED: 0.3861111111111111\n",
            "Prediction: <s_total><s_total_price>11,700</s_total_price><s_changeprice>8,300</s_changeprice><s_cashprice>20,000</s_cashprice></s_total><s_sub_total><s_subtotal_price>11,700</s_subtotal_price></s_sub_total><s_menu><s_price>6500</s_price><s_nm>TALAN UNGU</s_nm><s_cnt>1</s_cnt><sep/><s_price>19,500</s_price><s_nm>RICE ITEM</s_nm><s_cnt>40,000</s_cnt><sep/><s_price>7,800</s_price><s_nm>MISO</s_nm><s_cnt>80</s_cnt></s_menu>\n",
            "    Answer: <s_total><s_total_price>11,700</s_total_price><s_menuqty_cnt>4.00xITEMs</s_menuqty_cnt><s_changeprice>8,300</s_changeprice><s_cashprice>20,000</s_cashprice></s_total><s_sub_total><s_subtotal_price>11,700</s_subtotal_price></s_sub_total><s_menu><s_unitprice>@6500</s_unitprice><s_price>19,500</s_price><s_nm>TALAM UNGU</s_nm><s_discountprice>-7,800</s_discountprice><s_cnt>3X</s_cnt><sep/><s_unitprice>@0</s_unitprice><s_price>0</s_price><s_nm>MIKA KECIL</s_nm><s_cnt>1X</s_cnt></s_menu>\n",
            " Normed ED: 0.3065843621399177\n",
            "Pushing model to the hub, epoch 0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/45.3M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "76dfc462a32d408087330997a792e237"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/call.py:54: Detected KeyboardInterrupt, attempting graceful shutdown...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Inference\n",
        "\n",
        "At inference time, we use the [generate()](https://huggingface.co/docs/transformers/v4.41.2/en/main_classes/text_generation#transformers.GenerationMixin.generate) method to autoregressively generate text given an image + text prompt.\n",
        "\n",
        "We'll try it out on an image from the test set."
      ],
      "metadata": {
        "id": "mv0X11al5tCo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_example = dataset[\"test\"][0]\n",
        "test_image = test_example[\"image\"]\n",
        "test_image"
      ],
      "metadata": {
        "id": "hYwqZDqf52rD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 665
        },
        "outputId": "552bc5ce-afae-4e89-be00-ecc2b3c1bdd3"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=432x648>"
            ],
            "image/png": 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\n"
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can prepare the image along with the text prompt used during training using the processor:"
      ],
      "metadata": {
        "id": "NuHWJXxe6D4d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inputs = processor(text=PROMPT, images=test_image, return_tensors=\"pt\")\n",
        "for k,v in inputs.items():\n",
        "  print(k,v.shape)"
      ],
      "metadata": {
        "id": "qeJZBrwW6HE7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c883949b-eee3-4b1b-e959-5d98e4245bbe"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "input_ids torch.Size([1, 261])\n",
            "attention_mask torch.Size([1, 261])\n",
            "pixel_values torch.Size([1, 3, 224, 224])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, all we need to do is pass the inputs to the generate method.\n",
        "\n",
        "Thanks to the [PEFT integration](https://huggingface.co/docs/peft/tutorial/peft_integrations#transformers) in the Transformers library, the pre-trained model along with the adapters will be automatically loaded for you. One can optionally merge the adapters into the base model by calling the [merge_and_unload](https://huggingface.co/docs/peft/main/en/developer_guides/lora#merge-adapters) method."
      ],
      "metadata": {
        "id": "5AYsK2o4TCNS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import PaliGemmaForConditionalGeneration\n",
        "\n",
        "model = PaliGemmaForConditionalGeneration.from_pretrained(FINETUNED_MODEL_ID)\n",
        "\n",
        "# Autoregressively generate\n",
        "# We use greedy decoding here, for more fancy methods see https://huggingface.co/blog/how-to-generate\n",
        "generated_ids = model.generate(**inputs, max_new_tokens=MAX_LENGTH)\n",
        "\n",
        "# Next we turn each predicted token ID back into a string using the decode method\n",
        "# We chop of the prompt, which consists of image tokens and our text prompt\n",
        "image_token_index = model.config.image_token_index\n",
        "num_image_tokens = len(generated_ids[generated_ids==image_token_index])\n",
        "num_text_tokens = len(processor.tokenizer.encode(PROMPT))\n",
        "num_prompt_tokens = num_image_tokens + num_text_tokens + 2\n",
        "generated_text = processor.batch_decode(generated_ids[:, num_prompt_tokens:], skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]\n",
        "generated_text"
      ],
      "metadata": {
        "id": "OoO-qFyGPkFH",
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          "base_uri": "https://localhost:8080/",
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      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<s_total><s_total_price>60.000</s_total_price><s_changeprice>0.000</s_changeprice><s_cashprice>60.000</s_cashprice></s_total><s_menu><s_price>60.000</s_price><s_nm>TICKET CA</s_nm><s_cnt>2</s_cnt></s_menu>'"
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      "cell_type": "markdown",
      "source": [
        "We can convert it into JSON using the method below (taken from Donut):"
      ],
      "metadata": {
        "id": "eS46l7gvZoz2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "# let's turn that into JSON\n",
        "def token2json(tokens, is_inner_value=False, added_vocab=None):\n",
        "        \"\"\"\n",
        "        Convert a (generated) token sequence into an ordered JSON format.\n",
        "        \"\"\"\n",
        "        if added_vocab is None:\n",
        "            added_vocab = processor.tokenizer.get_added_vocab()\n",
        "\n",
        "        output = {}\n",
        "\n",
        "        while tokens:\n",
        "            start_token = re.search(r\"<s_(.*?)>\", tokens, re.IGNORECASE)\n",
        "            if start_token is None:\n",
        "                break\n",
        "            key = start_token.group(1)\n",
        "            key_escaped = re.escape(key)\n",
        "\n",
        "            end_token = re.search(rf\"</s_{key_escaped}>\", tokens, re.IGNORECASE)\n",
        "            start_token = start_token.group()\n",
        "            if end_token is None:\n",
        "                tokens = tokens.replace(start_token, \"\")\n",
        "            else:\n",
        "                end_token = end_token.group()\n",
        "                start_token_escaped = re.escape(start_token)\n",
        "                end_token_escaped = re.escape(end_token)\n",
        "                content = re.search(\n",
        "                    f\"{start_token_escaped}(.*?){end_token_escaped}\", tokens, re.IGNORECASE | re.DOTALL\n",
        "                )\n",
        "                if content is not None:\n",
        "                    content = content.group(1).strip()\n",
        "                    if r\"<s_\" in content and r\"</s_\" in content:  # non-leaf node\n",
        "                        value = token2json(content, is_inner_value=True, added_vocab=added_vocab)\n",
        "                        if value:\n",
        "                            if len(value) == 1:\n",
        "                                value = value[0]\n",
        "                            output[key] = value\n",
        "                    else:  # leaf nodes\n",
        "                        output[key] = []\n",
        "                        for leaf in content.split(r\"<sep/>\"):\n",
        "                            leaf = leaf.strip()\n",
        "                            if leaf in added_vocab and leaf[0] == \"<\" and leaf[-2:] == \"/>\":\n",
        "                                leaf = leaf[1:-2]  # for categorical special tokens\n",
        "                            output[key].append(leaf)\n",
        "                        if len(output[key]) == 1:\n",
        "                            output[key] = output[key][0]\n",
        "\n",
        "                tokens = tokens[tokens.find(end_token) + len(end_token) :].strip()\n",
        "                if tokens[:6] == r\"<sep/>\":  # non-leaf nodes\n",
        "                    return [output] + token2json(tokens[6:], is_inner_value=True, added_vocab=added_vocab)\n",
        "\n",
        "        if len(output):\n",
        "            return [output] if is_inner_value else output\n",
        "        else:\n",
        "            return [] if is_inner_value else {\"text_sequence\": tokens}"
      ],
      "metadata": {
        "id": "yGhs-QLtZovJ"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generated_json = token2json(generated_text)\n",
        "print(generated_json)"
      ],
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        },
        "id": "NYZsx9CNZ2Ik",
        "outputId": "b1f0ccc5-ebc6-4559-c6fa-b2ca732af4a3"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'total': {'total_price': '60.000', 'changeprice': '0.000', 'cashprice': '60.000'}, 'menu': {'price': '60.000', 'nm': 'TICKET CA', 'cnt': '2'}}\n"
          ]
        }
      ]
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